The optimization process is composed of 3000 epochs (E). During each epoch, the major fraction (70%) of the \(\mathbf {T}\)  (the actual training set) is “back-propagated” through the ANN in batches of size 1024 (B). The stochastic gradient-based optimizer (Opt) Adam [1]} is employed in the optimization. Particularly, the optimizer updates the ANN weights based on the gradient computed on each random batch (during each epoch). The initial learning rate (InitLR) is \(10^{-3}\) , with a decaying rate (DecR) of 0.1 and a decaying step (DecS) of 1000 epochs. The details are reported in tab: ANNandOptimizationDetails.
